OpenRaiser/Intern-Atlas huggingface dataset:
Tora2: Motion and Appearance Customized Diffusion Transformer for Multi-Entity Video Generation
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in diffusion transformer models for motion-guided video generation, such as Tora, have shown significant progress. In this paper, we present Tora2, an enhanced version of Tora, which introduces several design improvements to expand its capabilities in both appearance and motion customization. Specifically, we introduce a decoupled personalization extractor that generates comprehensive personalization embeddings for multiple open-set entities, better preserving fine-grained visual details compared to previous methods. Building on this, we design a gated self-attention mechanism to integrate trajectory, textual description, and visual information for each entity. This innovation significantly reduces misalignment in multimodal conditioning during training. Moreover, we introduce a contrastive loss that jointly optimizes trajectory dynamics and entity consistency through explicit mapping between motion and personalization embeddings. Tora2 is, to our best knowledge, the first method to achieve simultaneous multi-entity customization of appearance and motion for video generation. Experimental results demonstrate that Tora2 achieves competitive performance with state-of-the-art customization methods while providing advanced motion control capabilities, which marks a critical advancement in multi-condition video generation. Project page: https://ali-videoai.github.io/Tora2_page/.
Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models
Venue: arXiv cs.SE 2025
Year: 2025

Context: Large Language Model (LLM) agents are becoming widely used for various Requirements Engineering (RE) tasks. Research on improving their accuracy mainly focuses on prompt engineering, model fine-tuning, and retrieval augmented generation. However, these methods often treat models as isolated black boxes - relying on single-pass outputs without iterative refinement or collaboration, limiting robustness and adaptability. Objective: We propose that, just as human debates enhance accuracy and reduce bias in RE tasks by incorporating diverse perspectives, different LLM agents debating and collaborating may achieve similar improvements. Our goal is to investigate whether Multi-Agent Debate (MAD) strategies can enhance RE performance. Method: We conducted a systematic study of existing MAD strategies across various domains to identify their key characteristics. To assess their applicability in RE, we implemented and tested a preliminary MAD-based framework for RE classification. Results: Our study identified and categorized several MAD strategies, leading to a taxonomy outlining their core attributes. Our preliminary evaluation demonstrated the feasibility of applying MAD to RE classification. Conclusions: MAD presents a promising approach for improving LLM accuracy in RE tasks. This study provides a foundational understanding of MAD strategies, offering insights for future research and refinements in RE applications.
Evolution without Large Models: Training Language Model with Task Principles
Venue: arXiv cs.CL 2025
Year: 2025

A common training approach for language models involves using a large-scale language model to expand a human-provided dataset, which is subsequently used for model training.This method significantly reduces training costs by eliminating the need for extensive human data annotation. However, it still faces challenges such as high carbon emissions during data augmentation and the risk of data leakage when we use closed-source LLMs. To address these issues, we propose a self-evolution method for language models. First, we introduce the Multi-level Principle Generation, which enables a large-scale model to summarize task-completion principles based on a small amount of task data. Then, we propose the Principle-based Instance Generation, in which a smaller-scale language model uses these task principles to generate a large amount of data. This data is then used for model training. Experimental results show that our proposed method significantly improves model performance compared to directly using a smaller-scale language model to generate data. Additionally, since we only use the large-scale language model to generate the task-completion principles, the carbon emissions associated with training the model are greatly reduced.
Conditional Multi-Stage Failure Recovery for Embodied Agents
Venue: arXiv cs.CL 2025
Year: 2025

Embodied agents performing complex tasks are susceptible to execution failures, motivating the need for effective failure recovery mechanisms. In this work, we introduce a conditional multistage failure recovery framework that employs zero-shot chain prompting. The framework is structured into four error-handling stages, with three operating during task execution and one functioning as a post-execution reflection phase. Our approach utilises the reasoning capabilities of LLMs to analyse execution challenges within their environmental context and devise strategic solutions. We evaluate our method on the TfD benchmark of the TEACH dataset and achieve state-of-the-art performance, outperforming a baseline without error recovery by 11.5% and surpassing the strongest existing model by 19%.
Rethinking Pricing in Energy Markets: Pay-as-Bid vs Pay-as-Clear
Venue: arXiv cs.GT 2025
Year: 2025

The design of energy markets is a subject of ongoing debate, particularly concerning the choice between the widely adopted Pay-as-Clear (PC) pricing mechanism and the alternative Pay-as-Bid (PB). These mechanisms determine how energy producers are compensated: under PC, all selected producers are paid the market-clearing price (i.e., the highest accepted bid), while under PB, each selected producer is paid their own submitted bid. The overarching objective is to meet the total demand for energy at minimal cost in the presence of strategic behavior. We present two key theoretical results. First, no mechanism can uniformly dominate PC or PB. This means that for any mechanism $\mathcal{M}$, there exists a market configuration and a mixed-strategy Nash equilibrium of PC (respectively for PB) that yields strictly lower total energy costs than under $\mathcal{M}$. Second, in terms of worst-case equilibrium outcomes, PB consistently outperforms PC: across all market instances, the highest possible equilibrium price under PB is strictly lower than that under PC. This suggests a structural robustness of PB to strategic manipulation. These theoretical insights are further supported by extensive simulations based on no-regret learning dynamics, which consistently yield lower average market prices in several energy market settings.
SCCRUB: Surface Cleaning Compliant Robot Utilizing Bristles
Venue: arXiv cs.RO 2025
Year: 2025

Scrubbing surfaces is a physically demanding and time-intensive task. Removing adhered contamination requires substantial friction generated through pressure and torque or high lateral forces. Rigid robotic manipulators, while capable of exerting these forces, are usually confined to structured environments isolated from humans due to safety risks. In contrast, soft robot arms can safely work around humans and adapt to environmental uncertainty, but typically struggle to transmit the continuous torques or lateral forces necessary for scrubbing. Here, we demonstrate a soft robotic arm scrubbing adhered residues using torque and pressure, a task traditionally challenging for soft robots. We train a neural network to learn the arm's inverse kinematics and elasticity, which enables open-loop force and position control. Using this learned model, the robot successfully scrubbed burnt food residue from a plate and sticky fruit preserve from a toilet seat, removing an average of 99.7% of contamination. This work demonstrates how soft robots, capable of exerting continuous torque, can effectively and safely scrub challenging contamination from surfaces.
AI-Based Demand Forecasting and Load Balancing for Optimising Energy use in Healthcare Systems: A real case study
Venue: arXiv cs.AI 2025
Year: 2025

This paper tackles the urgent need for efficient energy management in healthcare facilities, where fluctuating demands challenge operational efficiency and sustainability. Traditional methods often prove inadequate, causing inefficiencies and higher costs. To address this, the study presents an AI-based framework combining Long Short-Term Memory (LSTM), genetic algorithm (GA), and SHAP (Shapley Additive Explanations), specifically designed for healthcare energy management. Although LSTM is widely used for time-series forecasting, its application in healthcare energy prediction remains underexplored. The results reveal that LSTM significantly outperforms ARIMA and Prophet models in forecasting complex, non-linear demand patterns. LSTM achieves a Mean Absolute Error (MAE) of 21.69 and Root Mean Square Error (RMSE) of 29.96, far better than Prophet (MAE: 59.78, RMSE: 81.22) and ARIMA (MAE: 87.73, RMSE: 125.22), demonstrating superior performance. The genetic algorithm is applied to optimize model parameters and improve load balancing strategies, enabling adaptive responses to real-time energy fluctuations. SHAP analysis further enhances model transparency by explaining the influence of different features on predictions, fostering trust in decision-making processes. This integrated LSTM-GA-SHAP approach offers a robust solution for improving forecasting accuracy, boosting energy efficiency, and advancing sustainability in healthcare facilities. Future research may explore real-time deployment and hybridization with reinforcement learning for continuous optimization. Overall, the study establishes a solid foundation for using AI in healthcare energy management, highlighting its scalability, efficiency, and resilience potential.
Nyay-Darpan: Enhancing Decision Making Through Summarization and Case Retrieval for Consumer Law in India
Venue: arXiv cs.IR 2025
Year: 2025

AI-based judicial assistance and case prediction have been extensively studied in criminal and civil domains, but remain largely unexplored in consumer law, especially in India. In this paper, we present Nyay-Darpan, a novel two-in-one framework that (i) summarizes consumer case files and (ii) retrieves similar case judgements to aid decision-making in consumer dispute resolution. Our methodology not only addresses the gap in consumer law AI tools but also introduces an innovative approach to evaluate the quality of the summary. The term 'Nyay-Darpan' translates into 'Mirror of Justice', symbolizing the ability of our tool to reflect the core of consumer disputes through precise summarization and intelligent case retrieval. Our system achieves over 75 percent accuracy in similar case prediction and approximately 70 percent accuracy across material summary evaluation metrics, demonstrating its practical effectiveness. We will publicly release the Nyay-Darpan framework and dataset to promote reproducibility and facilitate further research in this underexplored yet impactful domain.
Taming Data Challenges in ML-based Security Tasks: Lessons from Integrating Generative AI
Venue: arXiv cs.CR 2025
Year: 2025

Machine learning-based supervised classifiers are widely used for security tasks, and their improvement has been largely focused on algorithmic advancements. We argue that data challenges that negatively impact the performance of these classifiers have received limited attention. We address the following research question: Can developments in Generative AI (GenAI) address these data challenges and improve classifier performance? We propose augmenting training datasets with synthetic data generated using GenAI techniques to improve classifier generalization. We evaluate this approach across 7 diverse security tasks using 6 state-of-the-art GenAI methods and introduce a novel GenAI scheme called Nimai that enables highly controlled data synthesis. We find that GenAI techniques can significantly improve the performance of security classifiers, achieving improvements of up to 32.6% even in severely data-constrained settings (only ~180 training samples). Furthermore, we demonstrate that GenAI can facilitate rapid adaptation to concept drift post-deployment, requiring minimal labeling in the adjustment process. Despite successes, our study finds that some GenAI schemes struggle to initialize (train and produce data) on certain security tasks. We also identify characteristics of specific tasks, such as noisy labels, overlapping class distributions, and sparse feature vectors, which hinder performance boost using GenAI. We believe that our study will drive the development of future GenAI tools designed for security tasks.
Large Language Models Predict Human Well-being -- But Not Equally Everywhere
Venue: arXiv cs.HC 2025
Year: 2025

Subjective well-being is a key metric in economic, medical, and policy decision-making. As artificial intelligence provides scalable tools for modelling human outcomes, it is crucial to evaluate whether large language models (LLMs) can accurately predict well-being across diverse global populations. We evaluate four leading LLMs using data from 64,000 individuals in 64 countries. While LLMs capture broad correlates such as income and health, their predictive accuracy decreases in countries underrepresented in the training data, highlighting systematic biases rooted in global digital and economic inequality. A pre-registered experiment demonstrates that LLMs rely on surface-level linguistic similarity rather than conceptual understanding, leading to systematic misestimations in unfamiliar or resource-limited settings. Injecting findings from underrepresented contexts substantially enhances performance, but a significant gap remains. These results highlight both the promise and limitations of LLMs in predicting global well-being, underscoring the importance of robust validation prior to their implementation across these areas.
Evaluation of Habitat Robotics using Large Language Models
Venue: arXiv cs.RO 2025
Year: 2025

This paper focuses on evaluating the effectiveness of Large Language Models at solving embodied robotic tasks using the Meta PARTNER benchmark. Meta PARTNR provides simplified environments and robotic interactions within randomized indoor kitchen scenes. Each randomized kitchen scene is given a task where two robotic agents cooperatively work together to solve the task. We evaluated multiple frontier models on Meta PARTNER environments. Our results indicate that reasoning models like OpenAI o3-mini outperform non-reasoning models like OpenAI GPT-4o and Llama 3 when operating in PARTNR's robotic embodied environments. o3-mini displayed outperform across centralized, decentralized, full observability, and partial observability configurations. This provides a promising avenue of research for embodied robotic development.
AnthroTAP: Learning Point Tracking with Real-World Motion
Venue: arXiv cs.CV 2025
Year: 2025

Point tracking models often struggle to generalize to real-world videos because large-scale training data is predominantly synthetic--the only source currently feasible to produce at scale. Collecting real-world annotations, however, is prohibitively expensive, as it requires tracking hundreds of points across frames. We introduce AnthroTAP, an automated pipeline that generates large-scale pseudo-labeled point tracking data from real human motion videos. Leveraging the structured complexity of human movement-non-rigid deformations, articulated motion, and frequent occlusions-AnthroTAP fits Skinned Multi-Person Linear (SMPL) models to detected humans, projects mesh vertices onto image planes, resolves occlusions via ray-casting, and filters unreliable tracks using optical flow consistency. A model trained on the AnthroTAP dataset achieves state-of-the-art performance on TAP-Vid, a challenging general-domain benchmark for tracking any point on diverse rigid and non-rigid objects (e.g., humans, animals, robots, and vehicles). Our approach outperforms recent self-supervised teacher-student models trained on vastly larger real datasets, while requiring only one day of training on 4 GPUs. AnthroTAP shows that structured human motion offers a scalable and effective source of real-world supervision for point tracking. Code and datasets will be made publicly available.
Wallets as Universal Access Devices
Venue: arXiv cs.CR 2025
Year: 2025

Wallets are access points for the digital economys value creation. Wallets for blockchains store the end-users cryptographic keys for administrating their digital assets and enable access to blockchain Web3 systems. Web3 delivers new service opportunities. This chapter focuses on the Web3 enabled release of value through the lens of wallets. Wallets may be implemented as software apps on smartphones, web apps on desktops, or hardware devices. Wallet users request high security, ease of use, and access of relevance from their wallets. Increasing connectivity, functionality, autonomy, personal support, and offline capability make the wallet into the user's Universal Access Device for any digital asset. Through wallet based services, the owner obtains enhanced digital empowerment. The new Web3 solutionareas, Identity and Decentralisation, enable considerable societal effects, and wallets are an integral part of these. One example is self sovereign identity solutions combined with wallet borne AI for personalised support, empowering the enduser beyond anything previously known. Improved welfare is foreseen globally through enlarged markets with collaborative services with drastically lowered transaction costs compared to today, the expected vastly increased levels of automation in society necessitate enhanced enduser protection. As wallets are considered a weak spot for security, improving overall security through blockchains is essential.
The Emotional Alignment Design Policy
Venue: arXiv cs.CY 2025
Year: 2025

According to what we call the Emotional Alignment Design Policy, artificial entities should be designed to elicit emotional reactions from users that appropriately reflect the entities' capacities and moral status, or lack thereof. This principle can be violated in two ways: by designing an artificial system that elicits stronger or weaker emotional reactions than its capacities and moral status warrant (overshooting or undershooting), or by designing a system that elicits the wrong type of emotional reaction (hitting the wrong target). Although presumably attractive, practical implementation faces several challenges including: How can we respect user autonomy while promoting appropriate responses? How should we navigate expert and public disagreement and uncertainty about facts and values? What if emotional alignment seems to require creating or destroying entities with moral status? To what extent should designs conform to versus attempt to alter user assumptions and attitudes?
Too Human to Model:The Uncanny Valley of LLMs in Social Simulation -- When Generative Language Agents Misalign with Modelling Principles
Venue: arXiv cs.CY 2025
Year: 2025

Large language models (LLMs) have been increasingly used to build agents in social simulation because of their impressive abilities to generate fluent, contextually coherent dialogues. Such abilities can enhance the realism of models. However, the pursuit of realism is not necessarily compatible with the epistemic foundation of modelling. We argue that LLM agents, in many regards, are too human to model: they are too expressive, detailed and intractable to be consistent with the abstraction, simplification, and interpretability typically demanded by modelling. Through a model-building thought experiment that converts the Bass diffusion model to an LLM-based variant, we uncover five core dilemmas: a temporal resolution mismatch between natural conversation and abstract time steps; the need for intervention in conversations while avoiding undermining spontaneous agent outputs; the temptation to introduce rule-like instructions in prompts while maintaining conversational naturalness; the tension between role consistency and role evolution across time; and the challenge of understanding emergence, where system-level patterns become obscured by verbose micro textual outputs. These dilemmas steer the LLM agents towards an uncanny valley: not abstract enough to clarify underlying social mechanisms, while not natural enough to represent realistic human behaviour. This exposes an important paradox: the realism of LLM agents can obscure, rather than clarify, social dynamics when misapplied. We tease out the conditions in which LLM agents are ideally suited: where system-level emergence is not the focus, linguistic nuances and meaning are central, interactions unfold in natural time, and stable role identity is more important than long-term behavioural evolution. We call for repositioning LLM agents in the ecosystem of social simulation for future applications.
SymFlux: deep symbolic regression of Hamiltonian vector fields
Venue: arXiv cs.LG 2025
Year: 2025

We present SymFlux, a novel deep learning framework that performs symbolic regression to identify Hamiltonian functions from their corresponding vector fields on the standard symplectic plane. SymFlux models utilize hybrid CNN-LSTM architectures to learn and output the symbolic mathematical expression of the underlying Hamiltonian. Training and validation are conducted on newly developed datasets of Hamiltonian vector fields, a key contribution of this work. Our results demonstrate the model's effectiveness in accurately recovering these symbolic expressions, advancing automated discovery in Hamiltonian mechanics.
Development and Real-World Application of Commercial Motor Vehicle Safety Enforcement Dashboards
Venue: arXiv cs.CE 2025
Year: 2025

Commercial Motor Vehicle (CMV) safety is crucial in traffic management and public safety. CMVs account for numerous traffic incidents, so monitoring CMV safety and safety inspections is essential for ensuring safe and efficient highway movement. This paper presents the development and real-world application of CMV dashboards designed under the guidance of CMV safety enforcement professionals from the Maryland State Police (MSP), the Maryland Department of Transportation - State Highway Administration (MDOT - SHA), and the Federal Motor Carrier Safety Administration (FMCSA) to enable intuitive and efficient analysis of CMV safety performance measures. First, three CMV safety dashboards enable CMV safety professionals to identify sites with a history of safety performance issues. A supplemental dashboard automates the analysis of CMV enforcement initiatives using the same performance measures. These performance measures are based on CMV probe vehicle speeds, inspection/citation data from Truck Weigh and Inspection Stations (TWIS), patrolling enforcement, and Virtual Weigh Stations (VWS). The authors collaborated with MSP to identify a portion of I-81 in Maryland, susceptible to improvement from targeted CMV enforcement. The supplemental enforcement assessment dashboard was employed to evaluate the impact of enforcement, including the post-enforcement halo effect. The results of the post-enforcement evaluation were mixed, indicating a need for more fine-grained citation data.